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Here, we introduce rctd, a supervised learning approach to decompose rna sequencing mixtures into single cell types, enabling the assignment of cell types to spatial. The authors demonstrate the strengths of rctd, cell2location, and spatialdwls for their performance, while also revealing the limitations of many methods when compared to. Robust cell type decomposition (rctd) is a statistical method for decomposing cell type mixtures in spatial transcriptomics data

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In this vignette, we will use a simulated. In this vignette, we will use a simulated dataset to. Here, we will explain how the analysis occured for our paper ‘robust decomposition of cell type mixtures in spatial transcriptomics’, which introduces and validates the rctd r package.

In this document, we run spacexr’s rctd algorithm on simple synthetic data to infer that the weights matrix should be interpreted as the proportion of rna molecules.

Spatial mapping of cell types with rctd enables defining spatial components of cellular identity, uncovering new principles of cellular organization in biological tissue Tools such as robust cell type decomposition (rctd) use weighted least squares optimization to refine cell type assignments while accounting for sequencing depth and noise.